19 research outputs found
Compressively Sensed Image Recognition
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201
MAMAF-Net: Motion-Aware and Multi-Attention Fusion Network for Stroke Diagnosis
Stroke is a major cause of mortality and disability worldwide from which one
in four people are in danger of incurring in their lifetime. The pre-hospital
stroke assessment plays a vital role in identifying stroke patients accurately
to accelerate further examination and treatment in hospitals. Accordingly, the
National Institutes of Health Stroke Scale (NIHSS), Cincinnati Pre-hospital
Stroke Scale (CPSS) and Face Arm Speed Time (F.A.S.T.) are globally known tests
for stroke assessment. However, the validity of these tests is skeptical in the
absence of neurologists. Therefore, in this study, we propose a motion-aware
and multi-attention fusion network (MAMAF-Net) that can detect stroke from
multimodal examination videos. Contrary to other studies on stroke detection
from video analysis, our study for the first time proposes an end-to-end
solution from multiple video recordings of each subject with a dataset
encapsulating stroke, transient ischemic attack (TIA), and healthy controls.
The proposed MAMAF-Net consists of motion-aware modules to sense the mobility
of patients, attention modules to fuse the multi-input video data, and 3D
convolutional layers to perform diagnosis from the attention-based extracted
features. Experimental results over the collected StrokeDATA dataset show that
the proposed MAMAF-Net achieves a successful detection of stroke with 93.62%
sensitivity and 95.33% AUC score
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
Restoration of poor quality images with a blended set of artifacts plays a
vital role for a reliable diagnosis. Existing studies have focused on specific
restoration problems such as image deblurring, denoising, and exposure
correction where there is usually a strong assumption on the artifact type and
severity. As a pioneer study in blind X-ray restoration, we propose a joint
model for generic image restoration and classification: Restore-to-Classify
Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model
keeps any disease intact after the restoration. Therefore, this will naturally
lead to a higher diagnosis performance thanks to the improved X-ray image
quality. To accomplish this crucial objective, we define the restoration task
as an Image-to-Image translation problem from poor quality having noisy,
blurry, or over/under-exposed images to high quality image domain. The proposed
R2C-GAN model is able to learn forward and inverse transforms between the two
domains using unpaired training samples. Simultaneously, the joint
classification preserves the disease label during restoration. Moreover, the
R2C-GANs are equipped with operational layers/neurons reducing the network
depth and further boosting both restoration and classification performances.
The proposed joint model is extensively evaluated over the QaTa-COV19 dataset
for Coronavirus Disease 2019 (COVID-19) classification. The proposed
restoration approach achieves over 90% F1-Score which is significantly higher
than the performance of any deep model. Moreover, in the qualitative analysis,
the restoration performance of R2C-GANs is approved by a group of medical
doctors. We share the software implementation at
https://github.com/meteahishali/R2C-GAN
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multimodal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.Peer reviewe
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Coronavirus disease (Covid-19) has been the main agenda of the whole world
since it came in sight in December 2019. It has already caused thousands of
causalities and infected several millions worldwide. Any technological tool
that can be provided to healthcare practitioners to save time, effort, and
possibly lives has crucial importance. The main tools practitioners currently
use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction
(RT-PCR) and Computed Tomography (CT), which require significant time,
resources and acknowledged experts. X-ray imaging is a common and easily
accessible tool that has great potential for Covid-19 diagnosis. In this study,
we propose a novel approach for Covid-19 recognition from chest X-ray images.
Despite the importance of the problem, recent studies in this domain produced
not so satisfactory results due to the limited datasets available for training.
Recall that Deep Learning techniques can generally provide state-of-the-art
performance in many classification tasks when trained properly over large
datasets, such data scarcity can be a crucial obstacle when using them for
Covid-19 detection. Alternative approaches such as representation-based
classification (collaborative or sparse representation) might provide
satisfactory performance with limited size datasets, but they generally fall
short in performance or speed compared to Machine Learning methods. To address
this deficiency, Convolution Support Estimation Network (CSEN) has recently
been proposed as a bridge between model-based and Deep Learning approaches by
providing a non-iterative real-time mapping from query sample to ideally sparse
representation coefficient' support, which is critical information for class
decision in representation based techniques.Comment: 10 page
Early myocardial infarction detection over multi-view echocardiography
Myocardial infarction (MI) is the leading cause of mortality in the world. Its early diagnosis can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. The regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in that can be captured by echocardiography. However, assessing the motion only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 12 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the left ventricle wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the displacements, which are concatenated and fed into the classifiers to detect MI. The main contributions of this study are (1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, (2) improving the performance of the prior work of threshold-based APs by a machine learning based approach, and (3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. The proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography. The software implementation is shared at https://github.com/degerliaysen/MultiEchoAI.Peer reviewe
Reliable Covid-19 Detection using Chest X-Ray Images
Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.acceptedVersionPeer reviewe
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
Computer-aided diagnosis has become a necessity for accurate and immediate
coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the
spread of the virus. Numerous studies have proposed to use Deep Learning
techniques for COVID-19 diagnosis. However, they have used very limited chest
X-ray (CXR) image repositories for evaluation with a small number, a few
hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor
grade the severity of COVID-19 infection. For this purpose, recent studies
proposed to explore the activation maps of deep networks. However, they remain
inaccurate for localizing the actual infestation making them unreliable for
clinical use. This study proposes a novel method for the joint localization,
severity grading, and detection of COVID-19 from CXR images by generating the
so-called infection maps. To accomplish this, we have compiled the largest
dataset with 119,316 CXR images including 2951 COVID-19 samples, where the
annotation of the ground-truth segmentation masks is performed on CXRs by a
novel collaborative human-machine approach. Furthermore, we publicly release
the first CXR dataset with the ground-truth segmentation masks of the COVID-19
infected regions. A detailed set of experiments show that state-of-the-art
segmentation networks can learn to localize COVID-19 infection with an F1-score
of 83.20%, which is significantly superior to the activation maps created by
the previous methods. Finally, the proposed approach achieved a COVID-19
detection performance with 94.96% sensitivity and 99.88% specificity
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Coronavirus disease 2019 (COVID-19) has rapidly become a global health
concern after its first known detection in December 2019. As a result, accurate
and reliable advance warning system for the early diagnosis of COVID-19 has now
become a priority. The detection of COVID-19 in early stages is not a
straightforward task from chest X-ray images according to expert medical
doctors because the traces of the infection are visible only when the disease
has progressed to a moderate or severe stage. In this study, our first aim is
to evaluate the ability of recent \textit{state-of-the-art} Machine Learning
techniques for the early detection of COVID-19 from chest X-ray images. Both
compact classifiers and deep learning approaches are considered in this study.
Furthermore, we propose a recent compact classifier, Convolutional Support
Estimator Network (CSEN) approach for this purpose since it is well-suited for
a scarce-data classification task. Finally, this study introduces a new
benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage
COVID-19 pneumonia samples (very limited or no infection signs) labelled by the
medical doctors and 12 544 samples for control (normal) class. A detailed set
of experiments shows that the CSEN achieves the top (over 97%) sensitivity with
over 95.5% specificity. Moreover, DenseNet-121 network produces the leading
performance among other deep networks with 95% sensitivity and 99.74%
specificity.Comment: 12 page
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Myocardial infarction (MI), or commonly known as heart attack, is a
life-threatening health problem worldwide from which 32.4 million people suffer
each year. Early diagnosis and treatment of MI are crucial to prevent further
heart tissue damages or death. The earliest and most reliable sign of ischemia
is regional wall motion abnormality (RWMA) of the affected part of the
ventricular muscle. Echocardiography can easily, inexpensively, and
non-invasively exhibit the RWMA. In this article, we introduce a three-phase
approach for early MI detection in low-quality echocardiography: 1)
segmentation of the entire left ventricle (LV) wall using a state-of-the-art
deep learning model, 2) analysis of the segmented LV wall by feature
engineering, and 3) early MI detection. The main contributions of this study
are highly accurate segmentation of the LV wall from low-quality
echocardiography, pseudo labeling approach for ground-truth formation of the
unannotated LV wall, and the first public echocardiographic dataset (HMC-QU)*
for MI detection. Furthermore, the outputs of the proposed approach can
significantly help cardiologists for a better assessment of the LV wall
characteristics. The proposed approach has achieved 95.72% sensitivity and
99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03%
specificity, and 86.85% precision for MI detection on the HMC-QU dataset. *The
benchmark HMC-QU dataset is publicly shared at the repository
https://www.kaggle.com/aysendegerli/hmcqu-datase